pipelines/examples/pipelines/providers/litellm_manifold_pipeline.py
2024-08-02 15:51:28 -04:00

136 lines
4.6 KiB
Python

"""
title: LiteLLM Manifold Pipeline
author: open-webui
date: 2024-05-30
version: 1.0.1
license: MIT
description: A manifold pipeline that uses LiteLLM.
"""
from typing import List, Union, Generator, Iterator
from schemas import OpenAIChatMessage
from pydantic import BaseModel
import requests
import os
class Pipeline:
class Valves(BaseModel):
LITELLM_BASE_URL: str = ""
LITELLM_API_KEY: str = ""
LITELLM_PIPELINE_DEBUG: bool = False
def __init__(self):
# You can also set the pipelines that are available in this pipeline.
# Set manifold to True if you want to use this pipeline as a manifold.
# Manifold pipelines can have multiple pipelines.
self.type = "manifold"
# Optionally, you can set the id and name of the pipeline.
# Best practice is to not specify the id so that it can be automatically inferred from the filename, so that users can install multiple versions of the same pipeline.
# The identifier must be unique across all pipelines.
# The identifier must be an alphanumeric string that can include underscores or hyphens. It cannot contain spaces, special characters, slashes, or backslashes.
# self.id = "litellm_manifold"
# Optionally, you can set the name of the manifold pipeline.
self.name = "LiteLLM: "
# Initialize rate limits
self.valves = self.Valves(
**{
"LITELLM_BASE_URL": os.getenv(
"LITELLM_BASE_URL", "http://localhost:4001"
),
"LITELLM_API_KEY": os.getenv("LITELLM_API_KEY", "your-api-key-here"),
"LITELLM_PIPELINE_DEBUG": os.getenv("LITELLM_PIPELINE_DEBUG", False),
}
)
# Get models on initialization
self.pipelines = self.get_litellm_models()
pass
async def on_startup(self):
# This function is called when the server is started.
print(f"on_startup:{__name__}")
# Get models on startup
self.pipelines = self.get_litellm_models()
pass
async def on_shutdown(self):
# This function is called when the server is stopped.
print(f"on_shutdown:{__name__}")
pass
async def on_valves_updated(self):
# This function is called when the valves are updated.
self.pipelines = self.get_litellm_models()
pass
def get_litellm_models(self):
headers = {}
if self.valves.LITELLM_API_KEY:
headers["Authorization"] = f"Bearer {self.valves.LITELLM_API_KEY}"
if self.valves.LITELLM_BASE_URL:
try:
r = requests.get(
f"{self.valves.LITELLM_BASE_URL}/v1/models", headers=headers
)
models = r.json()
return [
{
"id": model["id"],
"name": model["name"] if "name" in model else model["id"],
}
for model in models["data"]
]
except Exception as e:
print(f"Error fetching models from LiteLLM: {e}")
return [
{
"id": "error",
"name": "Could not fetch models from LiteLLM, please update the URL in the valves.",
},
]
else:
print("LITELLM_BASE_URL not set. Please configure it in the valves.")
return []
def pipe(
self, user_message: str, model_id: str, messages: List[dict], body: dict
) -> Union[str, Generator, Iterator]:
if "user" in body:
print("######################################")
print(f'# User: {body["user"]["name"]} ({body["user"]["id"]})')
print(f"# Message: {user_message}")
print("######################################")
headers = {}
if self.valves.LITELLM_API_KEY:
headers["Authorization"] = f"Bearer {self.valves.LITELLM_API_KEY}"
try:
payload = {**body, "model": model_id, "user": body["user"]["id"]}
payload.pop("chat_id", None)
payload.pop("user", None)
payload.pop("title", None)
r = requests.post(
url=f"{self.valves.LITELLM_BASE_URL}/v1/chat/completions",
json=payload,
headers=headers,
stream=True,
)
r.raise_for_status()
if body["stream"]:
return r.iter_lines()
else:
return r.json()
except Exception as e:
return f"Error: {e}"